An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization

Gu, Yunting; Lv, Jinguang; Bo, Jun; Zhao, Baixuan; Zheng, Kaifeng; Zhao, Yuan; Jin, Tao; Qin, Yuxin; Wang, Weibiao; Liang, Jingqiu

doi:10.3390/app12073558

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…After filtering the papers, using the inclusion and exclusion criteria already discussed, 24 papers remained to be reviewed, listed in Table 2. Among them, 19 papers were found that are specifically dedicated to edge detection, either using multiple descriptors extraction and aggregation or based on fuzzy set theory: type-2 fuzzy and neutrosophic sets [44,47,[80][81][82][83][84][85][86][87][88][89][90] or works that use clustering and pre-aggregation functions, which are dedicated to region segmentation, but consider that their characteristics can be extended to the task of edge detection [46,48,51,52,91,92]. The found methods of segmentation or edge detection, based on aggregation and pre-aggregation functions, can be divided into three groups: (i) based on the aggregation of distance functions and FCM, (ii) multiple descriptors extraction and aggregation, and (iii) based on fuzzy set theory: type-2 fuzzy and neutrosophic sets.…”

Section: Resultsmentioning

confidence: 99%

“…In [44,[80][81][82]93], the authors present an edge detection method inspired by the way that the human visual system works. The central idea of this approach lies in the integration of several types of global or local information features, such as brightness, color, and the relationship between these descriptors.…”

Section: Multiple Descriptors Extraction and Aggregationmentioning

confidence: 99%

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Systematic Review of Aggregation Functions Applied to Image Edge Detection

Amorim

Dimuro

Borges

et al. 2023

Axioms

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Edge detection is a crucial process in numerous stages of computer vision. This field of study has recently gained momentum due to its importance in various applications. The uncertainty, among other characteristics of images, makes it difficult to accurately determine the edge of objects. Furthermore, even the definition of an edge is vague as an edge can be considered as the maximum boundary between two regions with different properties. Given the advancement of research in image discontinuity detection, especially using aggregation and pre-aggregation functions, and the lack of systematic literature reviews on this topic, this paper aims to gather and synthesize the current state of the art of this topic. To achieve this, this paper presents a systematic review of the literature, which selected 24 papers filtered from 428 articles found in computer databases in the last seven years. It was possible to synthesize important related information, which was grouped into three approaches: (i) based on both multiple descriptor extraction and data aggregation, (ii) based on both the aggregation of distance functions and fuzzy C-means, and (iii) based on fuzzy theory, namely type-2 fuzzy and neutrosophic sets. As a conclusion, this review provides interesting gaps that can be explored in future work.

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Section: Resultsmentioning

confidence: 99%

Section: Multiple Descriptors Extraction and Aggregationmentioning

confidence: 99%

Systematic Review of Aggregation Functions Applied to Image Edge Detection

Amorim

Dimuro

Borges

et al. 2023

Axioms

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“…For obstacle recognition, Chen [28] proposed an adaptive object recognition system, which can effectively identify specific targets under complex backgrounds. For the extraction of edge information, Gu [29] used the improved wavelet mode maximum algorithm to extract image edges, which can obtain edge image information with better clarity and connectivity. Yu [30] extracted the boundary of an obstacle from the semantic segmentation result by applying pixel filtering.…”

Section: Obtaining Initial Informationmentioning

confidence: 99%

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

Jia,

Zeng,

Feng

et al. 2023

Applied Sciences

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In this paper, a novel global time-varying path planning (GTVP) method is proposed. In the method, real-time paths can be generated based on tunable Bezier curves, which can realize obstacle avoidance of manipulators. First, finite feature points are extracted to represent the obstacle information according to the shape information and position information of the obstacle. Then, the feature points of the obstacle are converted into the feature points of the curve, according to the scale coefficient and the center point of amplification. Furthermore, a Bezier curve representing the motion path at this moment is generated to realize real-time adjustment of the path. In addition, the 5-degree Bezier curve planning method consider the start direction and the end direction is used in the path planning to avoid the situation of abrupt change with oscillation of the trajectory. Finally, the GTVP method is applied to multi-obstacle environment to realize global time-varying dynamic path planning. Through theoretical derivation and simulation, it can be proved that the path planned by the GTVP method can meet the performance requirements of global regulation, real-time change and multi-obstacle avoidance simultaneously.

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“…Polarization image fusion methods are mainly divided into two categories: conventional fusion methods and neural network fusion methods. In 2015, Liu et al [7] proposed a combined MST and SR image fusion framework, simultaneously overcoming the inherent defects of MST and SR-based fusion methods; in 2022, Gu et al [8] proposed an improved wavelet mode maximum edge algorithm for fusing intensity and polarization images, which can obtain fused images with sharper edges.…”

Section: Introductionmentioning

confidence: 99%

Polarization image fusion method with image enhancement

Wang,

Ma,

Yan

et al. 2024

Phys. Scr.

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Because of their complementary characteristics, intensity images and polarization images are often fused to produce information-rich images. However, the polarization characteristics are easily affected by the object's environment, and the image fusion process may lose important information. In this paper, we propose an unsupervised end-to-end network framework based on a CNN for intensity images and degree of linear polarization images. First, we construct our own polarization dataset to solve the limitations of the training dataset; a hybrid loss function is designed to form an unsupervised learning process; and a Laplace operator enhancement layer is introduced into the network to further improve the quality of the fused images. Subjective and objective comparison experiments prove that the proposed fusion network is visually superior to several classical fusion methods.

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An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization

Cited by 8 publications

References 12 publications

Systematic Review of Aggregation Functions Applied to Image Edge Detection

Systematic Review of Aggregation Functions Applied to Image Edge Detection

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

Polarization image fusion method with image enhancement

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